The present exemplary embodiments disclosed herein relate generally to image processing. They find particular application in conjunction with classifying forms and background subtraction, and will be described with particular reference thereto. However, it is to be appreciated that the present exemplary embodiments are also amenable to other like applications.
Forms are a type of document that usually contains descriptive line-art to define data fields for entry of data. The spatial organization of data fields facilitates capturing data in a structured and organized fashion by human and automatic means. In a straightforward case, each data field can be cropped out of an image of the form and run through Optical Character Recognition (OCR) individually. This is called zonal OCR.
Zonal OCR works correctly when printed and/or handwritten data is confined to the correct locations on the form, as defined by the boundaries of the data fields. However, zonal OCR fails to work correctly when printed and/or handwritten data is misregistered with respect to the data fields. Data field definitions usually depend on a particular type of form that is used to establish the nominal locations. FIG. 1 provides an example of a form where the nominal locations of the data fields are demarked by the form line-art.
A common work flow for production imaging services is to classify batches of scanned form images into sets of multiple document types and extract the data items in the corresponding data field locations. Challenges with such processing are determining the document type of a given image, aligning (i.e., registering) the image to a template model corresponding to the document type, and subtracting the form line-art and field text descriptions in order to allow successful application of Zonal OCR. Ultimately, solutions to these problems require accurate document type determination, data field registration, and subtraction of printed background form information.
Known solutions perform template matching classification (usually with a subsampled version of the original image for fast processing) or by applying a discriminative classifier using low-level features extracted from the image pixels or connected components. Next, a registration step is performed mainly using corresponding points derived by matching image blocks to special fiducial markings or image anchor templates. Finally, printed background form information removal is performed, which could be guided by a template model or by connected component filters. Connected component filters, however, usually wipe out key character features (such as dots, dashes, etc.), as well as parts of broken up characters in poor quality images.
The present application provides new and improved methods and systems which overcome the above-referenced challenges.